Gaussian processes in TensorFlow
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Updated
Jun 3, 2024 - Python
Gaussian processes in TensorFlow
Machine learning algorithms for many-body quantum systems
Collection of Monte Carlo (MC) and Markov Chain Monte Carlo (MCMC) algorithms applied on simple examples.
A batteries-included toolkit for the GPU-accelerated OpenMM molecular simulation engine.
Manifold Markov chain Monte Carlo methods in Python
A lightweight and performant implementation of HMC and NUTS in Python, spun out of the PyMC project.
Bayesian Deep Learning with Stochastic Gradient MCMC Methods
Markov Chain Monte Carlo MCMC methods are implemented in various languages (including R, Python, Julia, Matlab)
A straightforward Bayesian data fitting library
Exploration of metropolis-hastings (local) and Uli Wolff (cluster) algorithms on the Ising Model
A toolbox for inference of mixture models
This repo contains the code of Transitional Markov chain Monte Carlo algorithm
Generalised Bayesian inversion framework
Accelerating Monte Carlo methods for Bayesian inference in dynamical models
Lightweight Bayesian deep learning library for fast prototyping based on PyTorch
Code for 'Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks'.
Classical models implemented from a Markov operator's perspective
BISIP | Bayesian inversion of spectral induced polarization laboratory data
Discrete Array Variable Reversible jump MCMC
Provides tools for computing Monte Carlo standard errors (MCSE) in Markov chain Monte Carlo (MCMC). A python numpy implementation of mcmcse.r
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